Among the numerous cues,
motion is usually simple to obtain as it can be implicitly generated from unlabeled videos.
In this paper, we aim to exploit such cues for object segmentation in a self-supervised manner,
 zero human annotation is required for training.
At a high level, we aim to exploit the common fate principle,
with the basic assumption being that
elements tend to be perceived as a group if they move in the same direction at the same rate (have similar optical flow).
Specifically,
we tackle the problem by training a generative model that decomposes the optical flow into foreground (object) and background layers,
describing each as a homogeneous field, with discontinuities occurring only between layers.
We adopt a variant of the Transformer [1]},
with the self-attention being replaced by slot attention [2]},
where iterative grouping and binding have been built into the architecture.
With some critical architectural changes,
we show that pixels undergoing similar motion are grouped together and assigned to the same layer.
